COM SCI 260
Machine Learning Algorithms
Computer Science · 4 units · Graduate courses (200-299)
Problems of identifying patterns in data. Machine learning allows computers to learn potentially complex patterns from data and to make decisions based on these patterns. Introduction to fundamentals of this discipline to provide both conceptual grounding and practical experience with several learning algorithms. Techniques and examples used in areas such as healthcare, financial systems, commerce, and social networking.
Letter grading.
Requisites
Official UCLA wording
Recommended requisite: course 180.
BruinTree reads · Recommended
confidence 1.00 · from textRequires
Everything that has to come before this course, not just the courses named in the requisite above.
COM SCI 260
- COM SCI 180Introduction to Algorithms and Complexity
- COM SCI 32Introduction to Computer Science II
- COM SCI 31Introduction to Computer Science I
- MATH 61Introduction to Discrete Structures
- MATH 31BIntegration and Infinite Series
1 direct requisite. Showing 6 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
COM SCI 260
- COM SCI 260CDeep Learning
- COM SCI 260DLarge-Scale Machine Learning
2 courses list this as a requisite. The whole downstream is here — 2 courses over 1 level. Every course here opens its own tree.